DISCOVERING OPTIMUM METHOD TO EXTRACT DEPTH …€¦ · more detailed and precise bathymetric map,...

DISCOVERING OPTIMUM METHOD TO EXTRACT DEPTH INFORMATION FOR

NEARSHORE COASTAL WATERS FROM SENTINEL-2A IMAGERY- CASE STUDY:

NAYBAND BAY, IRAN

K. Kabiria,*

a Department of Marine Remote Sensing, Iranian National Institute for Oceanography and Atmospheric Science, Tehran, Iran

[email protected]

KEY WORDS: Remote Sensing; Nayband Bay; Bathymetry, Coastal Zone, Persian Gulf

ABSTRACT:

The capabilities of Sentinel-2A imagery to determine bathymetric information in shallow coastal waters were examined. In this

regard, two Sentinel-2A images (acquired on February and March 2016 in calm weather and relatively low turbidity) were selected

from Nayband Bay, located in the northern Persian Gulf. In addition, a precise and accurate bathymetric map for the study area were

obtained and used for both calibrating the models and validating the results. Traditional linear and ratio transform techniques, as well

as a novel integrated method, were employed to determine depth values. All possible combinations of the three bands (Band 2: blue

(458-523 nm), Band 3: green (543-578 nm), and Band 4: red (650-680 nm), spatial resolution: 10 m) have been considered (11

options) using the traditional linear and ratio transform techniques, together with 10 model options for the integrated method. The

accuracy of each model was assessed by comparing the determined bathymetric information with field measured values. The

correlation coefficients (R2), and root mean square errors (RMSE) for validation points were calculated for all models and for two

satellite images. When compared with the linear transform method, the method employing ratio transformation with a combination of

all three bands yielded more accurate results (R2Mac = 0.795, R2

Feb = 0.777, RMSEMac = 1.889 m, and RMSEFeb =2.039 m). Although

most of the integrated transform methods (specifically the method including all bands and band ratios) have yielded the highest

accuracy, these increments were not significant, hence the ratio transformation has selected as optimum method.

* Corresponding author

1. INTRODUCTION

The Updated and reliable bathymetric information for near-

shore coastal waters are essential for coastal management and

monitoring, and for mapping benthic habitats in shallow waters

(Chust et al., 2010; Kabiri et al., 2013 & 2014). So far,

numerous methods have been developed to measure the depth

values and subsequently to produce the bathymetric maps.

Among the methods, the most accurate and reliable ones are

utilizing the single and multi-beam echo sounders (Maleika et

al., 2012; Horta et al., 2014) and airborne LIght Detection And

Ranging (LIDAR) (Chust et al., 2010, Saylam et al., 2017), but

they are the most expensive techniques as well. A number of

lower cost methods, such as vertical beam echo-sounders

(VBES), are capable of producing bathymetric maps with

acceptable accuracy for coastal research (Sánchez-Carnero et

al., 2012).

The potential usefulness of remotely sensed satellite data has

been confirmed for mapping and monitoring coastal areas

(Moradi and Kabiri, 2015). In this regard, multispectral

remotely sensed data have been widely employed to determine

depth values in shallow coastal waters. Lyzenga (1978) was the

first to develop a method to estimate depth values using

multispectral satellite imagery. This method was able to

minimize the effect of variation in bottom types on the

determined depth values. Stumpf et al. (2003) subsequently

proposed a novel ratio transform method that had greater

capabilities for estimating depth values in deeper areas and it

was less sensitive to different bottom types when compared to

the traditional linear transform technique.

The utilization of high spatial resolution imagery (~2 m) such as

QuickBird and WorldView-2 can yield high accuracy and a

more detailed bathymetric map (Collin and Planes, 2011;

Eugenio et al., 2015, Halls and Costin, 2016); however, these

imageries are expensive and can cover a smaller area. By

contrast, the medium spatial resolution imageries such as

Landsat (30 m) has been available at no cost since 1985 and can

be used to produce reliable and updated medium resolution

bathymetric maps (Clark et al., 1987; Baban, 1993; Liceaga-

Correa and Euan-Avila, 2002). This capability has been further

improved since the launch of Landsat-8 in February 2013

(Pahlevan et al., 2014; Pacheco et al., 2015; Kabiri and Moradi,

2016; Kabiri, 2017). However, after launching Sentinel-2A

satellite on June 2015 with 10 m spatial resolution, it is

expected to observe an improvement in the ability of producing

more detailed and precise bathymetric map, where the Sentinel-

2A includes required spectral bands to determine depth values.

To examine this assumption, the main objective of this research

is to assess the capability of Sentinel-2A imagery to estimate

depth values in near-shore coastal waters. In this regard,

Nayband Bay (located in the northern Persian Gulf) was

selected as the study area, where an accurate and reliable

database from depth values is available for this region. This may

enable us to calibrate the different methods for estimation of

depth values and to evaluate their accuracies as well. At the first

step, the traditional linear and ratio transform methods proposed

by Stumpf et al. (2003) were both applied to two selected

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-4/W4, 2017 Tehran's Joint ISPRS Conferences of GI Research, SMPR and EOEC 2017, 7–10 October 2017, Tehran, Iran

This contribution has been peer-reviewed. https://doi.org/10.5194/isprs-archives-XLII-4-W4-105-2017 | © Authors 2017. CC BY 4.0 License.

105

Sentinel-2A images to retrieve depth values in each pixel, by

consideration of all possible combinations of bands and band

ratios (Band 2: blue (458-523 nm), Band 3: green (543-578

nm), and Band 4: red (650-680 nm), spatial resolution: 10 m).

Thereafter, a novel method was proposed that combined the

linear and ratio transform methods. This new method was then

tested to determine whether it would improve the estimated

depth values or not. The optimum method was then selected by

comparing the statistical indicators, such as RMSE and R2 for

the retrieved depth values obtained by applying all

abovementioned methods. The estimated results were

subsequently compared to the field measured data. The

flexibility and robustness of the methods were then evaluated by

assessing the estimated depth values from all three methods for

different depth values.

2. MATERIALS AND METHODS

2.1 Study Area

The selected study area was Nayband Bay (Fig. 1a), which is

located in the south of Iran (north of the Persian Gulf), between

latitudes 27° 23′ N–27° 30′ N and longitudes 52° 35′ E–52° 41′

E (Fig. 1b). The total area of this bay is ~70 km2 and the

perimeter is ~40 km. The areas with greater depths (~20 m) are

happened in the western parts, while the central parts have

depths of ~10 m. The climate of this area categorizes as

tropical, where monthly mean air temperatures fluctuate

between 16°C (January) and 35°C (July) during a year. The

water type of bay categorizes as Case II which means has

relatively higher turbidity in comparison with Open Ocean

waters (Case I). Our regular field observations in the study area

showed that usually the amount of water turbidity in

summertime is higher than wintertime.

Figure 1. Bathymetric map of the study area (Nayband Bay),

where XY refers to the UTM-Zone 39 (WGS-84) projection

system (a). Location of Nayband Bay in the south of Iran (b)

2.2 Remotely Sensed Data

Two Sentinel-2A satellite images were downloaded from the

USGS (United States Geological Survey) EarthExplorer portal

(https://earthexplorer.usgs.gov/). This portal has provided a

wide range of global remotely sensed data, most of which

(including Sentile-2A imagery) are freely available for

downloading. Two cloud-free images of the study area were

selected. The images acquired on February 15, 2016 and March

03, 2016 and they were downloaded in the format of a Level-1

GeoTIFF data product. Table 1 summarizes the information

about the satellite images selected for this study.

Sentinel-2A Entity ID 20160215T072839

Acquisition Start Date 2016-02-15T07:18:40.591Z

Acquisition End Date 2016-02-15T07:28:39.481Z

Cloud Coverage (%) 0.0021

Sentinel-2A Entity ID 20160303T071558

Acquisition Start Date 2016-03-03T07:05:38.253Z

Acquisition End Date 2016-03-03T07:15:58.790Z

Cloud Coverage (%) 0.5884

Tile Number T39RXL

Datum, Map Projection WGS84, UTM, 39N

Spatial Resolution 10 m (for R, G, B bands)

Scene Centre 27°31'15.51"N, 52°34'06.36"E

Table 1. The detailed information of two Sentinel-2A images

used for this study

(Source: USGS, https://earthexplorer.usgs.gov/)

The raw satellite images required some corrections during the

pre-processing steps before they could be used for further

processes. These pre-processes include radiometric,

atmospheric, and geometric corrections. The required

coefficients are provided by the ESA in a metadata file (XML

file) that can be found together with other files. Specifically for

this study, image processing analysis was performed using

ENVI® 5.3 software. This software has some special modules

for importing, displaying, and analysing Sentinel-2A imagery

based on the aforesaid metadata file. The first step was to

convert the raw digital numbers (DN values) to radiance values.

Subsequently, the fast line-of-sight atmospheric analysis of

hypercubes (FLAASH®) module was used to perform

atmospheric correction. The atmospheric model was selected as

tropical, and the aerosol model was indicated as maritime,

because it was aimed to analyse the image data in a marine area.

Other required settings, such as sensor type, sensor altitude,

ground elevation, flight time, and initial visibility, were selected

based on other existing metadata, data, and previous knowledge

about the study area. The output of this step is atmospherically

corrected reflectance values for each pixel of the Sentinel-2A

image. The original satellite images are geo-registered and

provided in the universal transverse Mercator (UTM)

projection, but they must be re-corrected based on existing

accurate ground control points (GCPs) to increase the precision

of the geo-locations. In doing so, the existing map (scale=

1:1,000) was used for geo-referencing the both satellite images

and minimizing the geometric errors.



106

2.3 Field Measured Bathymetric Records

A hydrographic field survey was conducted by the National

Cartographic Center of Iran (NCC) in 2013 to produce a

nautical chart of Nayband Bay. Point-wise depth data (Fig. 2)

were collected using a single beam echo-sounder coupled with a

differential global positioning system (DGPS), so that the

accuracy and precision of data are acceptable to generate

bathymetric maps at a scale of 1:1,000 or smaller. According to

the metadata of the map, the depth values of the surveyed points

are reduced to a chart datum with approximately the level of the

lowest astronomical tide. In this study, the original sounding

points were acquired and employed for statistical analysis. All

depth values were adjusted according to tidal information from

the study area (obtained from NCC, Department of

Hydrography) for the date and time of the satellite overpasses.

Subsequently, in order to perform a comparative analysis, the

point-wise bathymetric data were converted to a raster grid

format, in accordance with geometric properties (coordinate

system and pixel size) of the selected Sentinel-2A images. It

should be noted that there is a ~3 year time interval between the

field measurements and the date of the satellite imaging; hence,

the bathymetry of the area may have changed during this period.

However, our field measurements in Nayband Bay showed that

the effect of this variation in our computations is minor, as it

was infrequently greater than 0.5 m.

Figure 2. Original sounding points surveyed using single beam

echo-sounder by the National Cartographic Centre of Iran. The

density of points is higher in areas with rough topography and

lower in smooth topography areas

2.4 Retrieving Depth Values from Multispectral Bands

The methodology for extraction of depth values from

multispectral satellite images initialized by Lyzenga (1978,

1981) and then developed by Stumpf et al. (2003) satellite

images with higher spatial resolution. Stumpf et al. (2003)

recommended a novel method, called ratio transform to retrieve

depth values from multi bands which differed from the

traditional linear transform algorithm suggested by Lyzenga

(1978, 1985). In this study, the accuracy of both algorithms

where assessed using all possible combinations of three visible

bands of Sentinel-2A imagery [Band 2, Blue: 490 nm (B), Band

3, Green: 560 nm (G), and Band 4, Red: 665 nm (R)] with 10 m

spatial resolution.

2.4.1 Linear Transform Method

Based on this methodology, the depth (Z) values can be

determined by applying Eq. 1 (Lyzenga, 1978, 1985)

(1)

where

(2)

The Xj, and Xk values may be determined using equations

similar to Eq. 2. In Eqs. 1 and 2, Rw is the reflectance values of

water (which includes the bottom reflectance in optically

shallow waters), R∞ is the reflectance value of optically deep

water (water column reflectance), and λi is the ith spectral band,

whereas the constant a values (a0 to an, where n is numbers of

spectral bands) should be determined (usually using multiple

linear regression). In the first step, the R∞ values for all three

bands (R, G, and B bands) are computed by plotting the

reflectance values of all pixels within the study area versus the

referenced depth values (Fig. 3). According to the plots, the

minimum recorded reflectance values are 0.2, 0.4, and 0.6 for

R, G, and B bands, and are considered as R∞R

, R∞G

, and R∞B,

respectively. To convert reflectances to the form of percentage

values, they were multiplied by 100. Four possible

combinations of these bands, including: i) R, G, B; ii) R, G; iii)

R, B; and iv) B, G were used to determine the Z values. The

plots in Fig. 3 show no light penetration ability for any of the

three bands at depths of more than 15 m; hence, the pixels with

depths of more than 15 m were eliminated from computations.

Figure 3. Scaled reflectance values of blue, green, and red

bands versus depth values in all pixels of the satellite image of

the study area



107

Furthermore, any pixels consisting of noisy objects (usually

clouds and boats, and the very shallow areas) were also filtered

out (Fig. 4b). After the exclusion of these points, the total

number of remaining pixels was ~131000. One half of these

points was used for calibration of the models (determination of

unknown parameters), and the other half was used for validating

the results. Thus, ~65500 pixels (the blue pixels in Fig. 4a)

were randomly selected for use in multiple linear regressions for

determining the unknown ai parameters, and the other ~65500

pixels (the red pixels in Fig. 4a) were considered for validating

the results.

Figure 4. Randomly selected pixels for calibration of the models

(blue pixels) and validation of the results (red pixels). The white

pixels indicate very shallow coastal areas, clouds or other non-

desired points that have been eliminated from computations

In the following, the multiple linear regression model was used

to determine unknown parameters.

2.4.2 Ratio Transform Method

In comparison with the linear transform methodology, the ratio

transform method has been developed to be more robust over

variable sea bottom types (Stumpf et al., 2003). The original

proposed methodology was based on the ratio values of two

bands (usually the B and G bands). In this research, the

formulation was expanded to consider all three chosen bands of

Sentinel-2A imagery. According to this consideration, the depth

values can be calculated by applying Eq. 3.

(3)

where m1–m3 are tunable constants to scale the ratio to depth, n

is a fixed constant for all areas (n=100), and m0 is the offset for

a depth of 0 m. Similar to the linear transform method, the

values of unknown mi parameters were determined by applying

multiple linear regression. However, since Z values can be

determined by applying only one ratio formed by applying only

two bands, the total number of possible combinations of band

ratios is 7, including: i) B/G, B/R, G/R; ii) B/G, B/R; iii) B/G,

G/R; iv) B/R, G/R; v) B/G; vi) B/R; and vii) G/R. Finally, the

depth values for all selected pixels used for validation of the

results (the same points used in the previous method) were

calculated using Eq. 3 and using the calculated mi parameters.

2.4.3 Integrated transform method

The probable improvement in precision and accuracy of the

determined depth values was examined by developing an

overall model based on integration of the two abovementioned

transforms (Eq. 4). Similar to the linear and ratio transform

methods, the unknown parameters were calculated by applying

linear regression on the 10 selected integrated models. These

models were selected based on the results obtained from

previous analysis by combining highly correlated bands/band

ratios with depth values. The results obtained from these models

were compared with the linear and ratio transform methods, and

the three statistical indices were determined for these 10 models

as well.

(4)

3. RESULTS AND DISCUSSION

Table 2 summarizes the determined values for ai, mi, and bi

parameters for all 21 possible combinations of bands/band

ratios in the linear, ratio, and integrated transform methods. The

two statistical indicators (R2 and RMSE for the points used for

validation) for both satellite images are represented. The depth

values for all pixels selected for validation were determined

using the determined values for unknown parameters and for all

21 chosen models.

Particularly for the linear transform method, the results showed

the highest accuracy when all three bands were applied, and the

statistical indicators reflected this fact. By contrast, the lowest

accuracy was observed when utilizing the B and R bands

together. Other combination of B and G bands gave slightly less

accurate results than those obtained using all three bands;

however, this decrease may be negligible. This means that

although adding R band to the computations may increase the

final accuracy, this increment is negligible. However, in

comparison with linear transform models, the results acquired

from the ratio transform models gave the better accuracy when

all three bands were applied (option 5). Here, the lowest

accuracy was obtained for the two B, R bands. On the other

hand, the results achieved for the integrated transform method

demonstrated an increasing in the accuracy of the estimated

depth values when compared with the linear and ratio transform

methods. However, this increment was not noticeable in all 10

selected models, (Table 2), particularly in comparison with the

option 5 when all bands incorporated in ratio transform method.

However, among the selected band/band ratio combinations in

this method, options 12 and 19 had higher accuracies.



108

According to the previous studies, a high degree of turbidity is

considered the most undesirable parameter for retrieving

bathymetric data from multispectral imagery (Pacheco et al.,

2015, Kabiri, 2017). Typically, this parameter has higher values

in coastal waters (Case II) than in open oceans (Case I) due to

the highly colored dissolved organic matter (CDOM)

transported by river and estuary systems (Moore et al., 1999)

and other suspended particles. Conversely, the estimated depth

values are expected to be less accurate in deeper areas than in

shallower areas due to the attenuation of light in all bands

during as it passes through the water column. To assess that

which model is less sensitive to the variation of depth values,

the RMSE values was computed in different depth levels

including depths <2 m, 2-4 m, 4–8 m, and >8 m (Fig 5). As

seen in Fig 5, the best results obtained for the depth range

between 2 and 4 meter. This means that although with

increasing the depth values the accuracy of the models will be

decreased, the extremely shallow waters (<2 m) may decrease

the efficiency of the models as well, due to the high water

turbidity.

4. CONCLUSION AND SUMMARY

The Sentinel-2A launched on June 2015 differs somewhat from

previous similar satellite imagery. The present study attempted

to inspect the three different methodologies to extract depth

values from three visible bands (red, green, and blue) of

Sentinel-2A imagery. In doing so, linear regression, coupled

with field measured data, was used to determine the unknown

parameters of 21 model options for formerly developed linear

and ratio transform methodologies (Lyzenga 1978, 1985;

Stumpf et al., 2003), as well as for a novel integrated method.

Consequently, the statistical indicators demonstrated that the

ratio transform method including all three bands has more

proficiency in determining the depth values in coastal water

bodies with a high variation in bottom types, whereas using the

linear transform methods may lead to less accurate results.

However, the final results showed that the integrated models

proposed in this study have higher accuracy than the

conventional linear and ratio transform models, yet this

improvement is not major. Additionally, most of models had

higher accuracy in the areas with depth between 2 and 4 meter,

where the effects of water turbidity and attenuation of light in

water column on bottom reflectance values are minimal.

Figure 5. Computed RMSE values for all 21 model options in

different depth levels

Linear transform

No. Bands/Band Ratios a0 a1 a2 a3 R2Mac

RMSEMac

(m) R2

Feb RMSEFeb

(m)

1 B,G,R 16.126 5.273 -9.921 1.478 0.740 2.126 0.702 2.362

2 G,R 14.786 -6.024 3.547 0.661 2.427 0.742 2.207

3 B,R 16.582 -4.296 0.565 0.389 3.265 0.648 2.639

4 B,G 18.056 5.737 -9.838 0.736 2.142 0.655 2.488

Ratio transform

m0 m1 m2 m3

5 B/G,B/R,G/R -1415.12 1357.24 -1025.1 1070.49 0.795 1.889 0.777 2.039

6 B/G,B/R -148.04 161.066 -14.02 0.761 2.040 0.678 2.398

7 B/G,G/R -133.74 146.045 -13.504 0.760 2.043 0.672 2.425

8 B/R,G/R 22.671 121.12 -143.16 0.751 2.081 0.656 2.505

9 B/G -166.97 161.822 0.756 2.060 0.618 2.655

10 B/R 36.215 -23.45 0.014 4.141 0.015 4.179

11 G/R 115.48 -91.605 0.524 2.876 0.325 3.956

Integrated transform

b0 b1 b2 b3 b4 b5 b6

12 B,G,R,B/G,B/R,G/R -2178.9 -1.264 4.147 -1.653 2095.284 -1605.6 1666.153 0.801 1.860 0.706 2.271

13 B,G,R,B/G,G/R -180.08 -0.205 -2.66 4.007 146.016 24.298 0.771 1.995 0.639 2.531

14 B,G,R,B/R,G/R -19.840 0.422 -3.55 3.682 107.71 -93.000 0.770 2.013 0.633 2.555

15 B,G,R,B/G,B/R -141.27 0.087 -2.72 3.363 117.725 16.685 0.770 1.998 0.651 2.485

16 B,B/G,B/R,G/R -1610.83 0.524 1542.891 -1175.54 1227.843 0.797 1.878 0.757 2.132

17 G,B/G,B/R,G/R -1812.49 1.12 1732.744 -1320.37 1380.095 0.799 1.871 0.740 2.181

18 R,B/G,B/R,G/R -1565.74 1.070 1487.905 -1120.71 1179.866 0.797 1.880 0.760 2.117

19 B,G,B/G,B/R,G/R -2090.87 -1.72 3.72 1993.449 -1510.64 1580.985 0.801 1.862 0.727 2.201

20 B,R,B/G,B/R,G/R -1612.24 0.554 -0.069 1545.016 -1177.93 1229.733 0.797 1.878 0.756 2.132

21 G,R,B/G,B/R,G/R -2082.48 2.98 -2.754 2017.885 -1562.86 1610.973 0.800 1.864 0.694 2.316

Table 2. Determined parameters for all combinations of bands/band ratios for linear, ratio, and integrated transform methods



109

REFERENCES

Baban SM (1993) The evaluation of different algorithms for

bathymetric charting of lakes using Landsat imagery.

International Journal of Remote Sensing 14:2263-2273

Chust G, Grande M, Galparsoro I, Uriarte A, Borja A (2010)

Capabilities of the bathymetric Hawk Eye LiDAR for coastal

habitat mapping: a case study within a Basque estuary.

Estuarine, Coastal and Shelf Science 89:200-213

Clark RK, Fay TH, Walker CL (1987) Bathymetry calculations

with Landsat 4 TM imagery under a generalized ratio

assumption. DTIC Document.

Collin A, Planes S (2011) What is the value added of 4 bands

within the submetric remote sensing of tropical coastscape?

Quickbird-2 vs WorldView-2. 2165-2168 in Geoscience and

Remote Sensing Symposium (IGARSS), 2011 IEEE

International

Eugenio F, Marcello J, Martin J (2015) High-resolution maps of

bathymetry and benthic habitats in shallow-water environments

using multispectral remote sensing imagery. IEEE Transactions

on Geoscience and Remote Sensing 53(7): 3539-3549

Halls J, Costin K (2016) Submerged and Emergent Land Cover

and Bathymetric Mapping of Estuarine Habitats Using

WorldView-2 and LiDAR Imagery. Remote Sensing 8(9), 718

Horta J, Pacheco A, Moura D, Ferreira Ó (2014) Can

recreational echosounder-chartplotter systems be used to

perform accurate nearshore bathymetric surveys? Ocean

Dynamics 64:1555-1567

Kabiri K, Pradhan B, Samimi-Namin K, Moradi M (2013)

Detecting coral bleaching using QuickBird multi-temporal data:

a feasibility study at Kish Island, the Persian Gulf. Estuarine,

Coastal and Shelf Science 117:273-281

Kabiri K, Rezai H, Moradi M, Pourjomeh F (2014) Coral reefs

mapping using parasailing aerial photography-feasibility study:

Kish Island, Persian Gulf. Journal of coastal conservation 18(6):

691-699

Kabiri K, Moradi M (2016) Landsat-8 imagery to estimate

clarity in near-shore coastal waters: Feasibility study-Chabahar

Bay, Iran. Continental Shelf Research 125: 44-53

Kabiri K (2017) Accuracy assessment of near-shore bathymetry

information retrieved from Landsat-8 imagery. Earth Science

Informatics; In press, doi: 10.1007/s12145-017-0293-7

Liceaga-Correa M, Euan-Avila J (2002) Assessment of coral

reef bathymetric mapping using visible Landsat Thematic

Mapper data. International Journal of Remote Sensing 23:3-14

Lyzenga DR (1978) Passive remote sensing techniques for

mapping water depth and bottom features. Applied optics

17:379-383

Lyzenga DR (1981) Remote sensing of bottom reflectance and

water attenuation parameters in shallow water using aircraft and

Landsat data. International Journal of Remote Sensing 2:71-82

Lyzenga DR (1985) Shallow-water bathymetry using combined

lidar and passive multispectral scanner data. International

Journal of Remote Sensing 6:115-125

Maleika W, Palczynski M, Frejlichowski D (2012) Interpolation

methods and the accuracy of bathymetric seabed models based

on multibeam echosounder data. 466-475 Intelligent

information and database systems. Springer.

Moore WS (1999) The subterranean estuary: a reaction zone of

ground water and sea water. Marine Chemistry 65:111-125

Moradi M, Kabiri K (2015) Spatio-temporal variability of SST

and Chlorophyll-a from MODIS data in the Persian Gulf.

Marine Pollution Bulletin 98(1): 14-25

Pacheco A, Horta J, Loureiro C, Ferreira Ó (2015) Retrieval of

nearshore bathymetry from Landsat 8 images: A tool for coastal

monitoring in shallow waters. Remote Sensing of Environment

159:102-116

Pahlevan N, Lee Z, Wei J, Schaaf CB, Schott JR, Berk A

(2014) On-orbit radiometric characterization of OLI (Landsat-8)

for applications in aquatic remote sensing. Remote Sensing of

Environment 154:272-284

Sánchez-Carnero N, Rodríguez-Pérez D, Couñago E, Aceña A,

Freire J (2012) Using vertical Sidescan Sonar as a tool for

seagrass cartography. Estuarine, Coastal and Shelf Science

115:334-344

Saylam K, Brown RA, Hupp JR (2017) Assessment of depth

and turbidity with airborne Lidar bathymetry and multiband

satellite imagery in shallow water bodies of the Alaskan North

Slope. International Journal of Applied Earth Observation and

Geoinformation, 58:191-200

Stumpf RP, Holderied K, Sinclair M (2003) Determination of

water depth with high‐resolution satellite imagery over variable

bottom types. Limnology and Oceanography 48:547-556



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